Data hiding such as steganography and invisible watermarking has important applications
in copyright protection, privacy-preserved communication and content provenance. Existing …

Development and exploitation of technology have led to the further expansion and
complexity of digital crimes. On the other hand, the growing volume of data and …

Abstract Rapid advances in Generative Adversarial Networks (GANs) raise new challenges
for image attribution; detecting whether an image is synthetic and, if so, determining which …

We present EKILA; a decentralized framework that enables creatives to receive recognition
and reward for their contributions to generative AI (GenAI). EKILA proposes a robust visual …

Abstract Generative Adversarial Networks (GANs) can generate hyperrealistic face images
of synthetic identities based on a latent understanding of real images from a large training …

Images tell powerful stories but cannot always be trusted. Matching images back to trusted
sources (attribution) enables users to make a more informed judgment of the images they …

A great deal of the images found in scientific publications are retouched, reused, or
composed to enhance the quality of the presentation. In most instances, these edits are …

We propose VADER, a spatio-temporal matching, alignment, and change summarization
method to help fight misinformation spread via manipulated videos. VADER matches and …

This paper introduces a framework for Audio Provenance Analysis addressing the complex
challenge of analyzing heterogeneous sets of audio items without requiring any prior …

A dramatic rise in the flow of manipulated image content on the Internet has led to a prompt
response from the media forensics research community. New mitigation efforts leverage …